[{"label": "EVALUATE-FOR", "tokens": "Experimental results demonstrate that our proposed << algorithm >> presents substantially reduced computational complexity and improved [[ flexibility ]] at the cost of slightly decreased pixel accuracy , as compared with the work of Chen and Wang .", "h": ["flexibility"], "t": ["algorithm"]}, {"label": "EVALUATE-FOR", "tokens": "We show that our << method >> can greatly speed up the [[ training time ]] for stochastic attention networks in the domains of image classification and caption generation .", "h": ["training time"], "t": ["method"]}, {"label": "EVALUATE-FOR", "tokens": "Following recent developments in the [[ automatic evaluation ]] of << machine translation >> and document summarization , we present a similar approach , implemented in a measure called POURPRE , for automatically evaluating answers to definition questions .", "h": ["automatic evaluation"], "t": ["machine translation"]}, {"label": "EVALUATE-FOR", "tokens": "Our << system >> produces an accurate prediction of geometric context of video achieving 96 % [[ accuracy ]] across main geometric classes .", "h": ["accuracy"], "t": ["system"]}, {"label": "EVALUATE-FOR", "tokens": "The [[ accuracy ]] of the statistical method is reasonably good , comparable to << taggers >> for English .", "h": ["accuracy"], "t": ["taggers"]}, {"label": "USED-FOR", "tokens": "By using a careful distinction between the different notions of reference time based on -LRB- Kamp and Reyle , 1993 -RRB- , we propose a [[ solution ]] to this << problem >> , within the framework of DRT .", "h": ["solution"], "t": ["problem"]}, {"label": "USED-FOR", "tokens": "We describe how this information is used in a [[ prototype system ]] designed to support information workers ' access to a << pharmaceutical news archive >> as part of their industry watch function .", "h": ["prototype system"], "t": ["pharmaceutical news archive"]}, {"label": "USED-FOR", "tokens": "After introducing this [[ approach ]] to MT system design , and the basics of << monolingual UCG >> , we will show how the two can be integrated , and present an example from an implemented bi-directional English-Spanish fragment .", "h": ["approach"], "t": ["monolingual UCG"]}, {"label": "USED-FOR", "tokens": "The speed of the resulting program lies somewhere in the middle of the scale of existing << spelling-checkers >> for [[ English ]] and the main dictionary fits into the standard 360K floppy , whereas the number of recognized word forms exceeds 6 million -LRB- for Czech -RRB- .", "h": ["English"], "t": ["spelling-checkers"]}, {"label": "USED-FOR", "tokens": "While this task has much in common with paraphrases acquisition which aims to discover semantic equivalence between verbs , the main challenge of [[ entailment acquisition ]] is to capture << asymmetric , or directional , relations >> .", "h": ["entailment acquisition"], "t": ["asymmetric , or directional , relations"]}, {"label": "HYPONYM-OF", "tokens": "The [[ Interval Algebra -LRB- IA -RRB- ]] and a subset of the Region Connection Calculus -LRB- RCC -RRB- , namely RCC-8 , are the dominant << Artificial Intelligence approaches >> for representing and reasoning about qualitative temporal and topological relations respectively .", "h": ["Interval Algebra -LRB- IA -RRB-"], "t": ["Artificial Intelligence approaches"]}, {"label": "HYPONYM-OF", "tokens": "The system participated in all the tracks of the << segmentation bakeoff >> -- PK-open , [[ PK-closed ]] , AS-open , AS-closed , HK-open , HK-closed , MSR-open and MSR - closed -- and achieved the state-of-the-art performance in MSR-open , MSR-close and PK-open tracks .", "h": ["PK-closed"], "t": ["segmentation bakeoff"]}, {"label": "HYPONYM-OF", "tokens": "Our algorithm considers chordal QCNs and a new form of << partial consistency >> which we define as [[ \u25c6 G-consistency ]] .", "h": ["\u25c6 G-consistency"], "t": ["partial consistency"]}, {"label": "HYPONYM-OF", "tokens": "Comprehensive evaluation is performed on a variety of challenging << non-rigid surfaces >> including face , cloth and [[ people ]] .", "h": ["people"], "t": ["non-rigid surfaces"]}, {"label": "HYPONYM-OF", "tokens": "Furthermore , we introduce global variables in the model , which can represent << global properties >> such as translation , [[ scale ]] or viewpoint .", "h": ["scale"], "t": ["global properties"]}, {"label": "PART-OF", "tokens": "This paper shows how the process of fitting a lexicalized grammar to a domain can be automated to a great extent by using a << hybrid system >> that combines traditional knowledge-based techniques with a [[ corpus-based approach ]] .", "h": ["corpus-based approach"], "t": ["hybrid system"]}, {"label": "PART-OF", "tokens": "This paper proposes that sentence analysis should be treated as defeasible reasoning , and presents such a treatment for Japanese sentence analyses using an argumentation system by Konolige , which is a << formalization of defeasible reasoning >> , that includes [[ arguments ]] and defeat rules that capture defeasibility .", "h": ["arguments"], "t": ["formalization of defeasible reasoning"]}, {"label": "PART-OF", "tokens": "In this paper , we want to show how the [[ morphological component ]] of an existing << NLP-system for Dutch -LRB- Dutch Medical Language Processor - DMLP -RRB- >> has been extended in order to produce output that is compatible with the language independent modules of the LSP-MLP system -LRB- Linguistic String Project - Medical Language Processor -RRB- of the New York University .", "h": ["morphological component"], "t": ["NLP-system for Dutch -LRB- Dutch Medical Language Processor - DMLP -RRB-"]}, {"label": "PART-OF", "tokens": "The proposed << mechanism >> includes title-driven name recognition , adaptive dynamic word formation , [[ identification of 2-character and 3-character Chinese names without title ]] .", "h": ["identification of 2-character and 3-character Chinese names without title"], "t": ["mechanism"]}, {"label": "PART-OF", "tokens": "<< CriterionSM Online Essay Evaluation Service >> includes a capability that labels sentences in student writing with [[ essay-based discourse elements ]] -LRB- e.g. , thesis statements -RRB- .", "h": ["essay-based discourse elements"], "t": ["CriterionSM Online Essay Evaluation Service"]}, {"label": "FEATURE-OF", "tokens": "<< Synchronous dependency insertion grammars >> are a version of synchronous grammars defined on [[ dependency trees ]] .", "h": ["dependency trees"], "t": ["Synchronous dependency insertion grammars"]}, {"label": "FEATURE-OF", "tokens": "This paper investigates some [[ computational problems ]] associated with << probabilistic translation models >> that have recently been adopted in the literature on machine translation .", "h": ["computational problems"], "t": ["probabilistic translation models"]}, {"label": "FEATURE-OF", "tokens": "We present the computational model for POS learning , and present results for applying it to << Bulgarian >> , a Slavic language with relatively [[ free word order ]] and rich morphology .", "h": ["free word order"], "t": ["Bulgarian"]}, {"label": "FEATURE-OF", "tokens": "Machine reading is a relatively new field that features computer programs designed to read flowing text and extract [[ fact assertions ]] expressed by the << narrative content >> .", "h": ["fact assertions"], "t": ["narrative content"]}, {"label": "FEATURE-OF", "tokens": "We compile a new << dataset >> of [[ diagrams ]] with exhaustive annotations of constituents and relationships for over 5,000 diagrams and 15,000 questions and answers .", "h": ["diagrams"], "t": ["dataset"]}, {"label": "COMPARE", "tokens": "Specifically , this system is designed to deterministically choose between pronominalization , [[ superordinate substitution ]] , and << definite noun phrase reiteration >> .", "h": ["superordinate substitution"], "t": ["definite noun phrase reiteration"]}, {"label": "COMPARE", "tokens": "Achieving extremely high [[ detection rates ]] , rather than << low error >> , is not a task typically addressed by machine learning algorithms .", "h": ["detection rates"], "t": ["low error"]}, {"label": "COMPARE", "tokens": "Experimental results show that our [[ method ]] significantly outperforms state-of-the-art << syntactic relation-based methods >> by up to 20 % in MRR .", "h": ["method"], "t": ["syntactic relation-based methods"]}, {"label": "COMPARE", "tokens": "We validate the effectiveness of the proposed [[ joint filter ]] through extensive comparisons with << state-of-the-art methods >> .", "h": ["joint filter"], "t": ["state-of-the-art methods"]}, {"label": "COMPARE", "tokens": "We measured the quality of the paraphrases produced in an experiment , i.e. , -LRB- i -RRB- their grammaticality : at least 99 % correct sentences ; -LRB- ii -RRB- their equivalence in meaning : at least 96 % correct paraphrases either by meaning equivalence or entailment ; and , -LRB- iii -RRB- the amount of internal lexical and syntactical variation in a set of [[ paraphrases ]] : slightly superior to that of << hand-produced sets >> .", "h": ["paraphrases"], "t": ["hand-produced sets"]}, {"label": "CONJUNCTION", "tokens": "Because of its adaptive nature , Bayesian learning serves as a unified approach for the following four speech recognition applications , namely parameter smoothing , [[ speaker adaptation ]] , << speaker group modeling >> and corrective training .", "h": ["speaker adaptation"], "t": ["speaker group modeling"]}, {"label": "CONJUNCTION", "tokens": "The system combines an extended [[ two-level morphology ]] -LSB- Trost , 1991a ; Trost , 1991b -RSB- with a << feature-based word grammar >> building on a hierarchical lexicon .", "h": ["two-level morphology"], "t": ["feature-based word grammar"]}, {"label": "CONJUNCTION", "tokens": "Specifically , the following components of the system are described : the [[ syntactic analyzer ]] , based on a Procedural Systemic Grammar , the << semantic analyzer >> relying on the Conceptual Dependency Theory , and the dictionary .", "h": ["syntactic analyzer"], "t": ["semantic analyzer"]}, {"label": "CONJUNCTION", "tokens": "We present the computational model for POS learning , and present results for applying it to Bulgarian , a Slavic language with relatively [[ free word order ]] and << rich morphology >> .", "h": ["free word order"], "t": ["rich morphology"]}, {"label": "CONJUNCTION", "tokens": "The agreement in question involves number in [[ nouns ]] and << reflexive pronouns >> and is syntactic rather than semantic in nature because grammatical number in English , like grammatical gender in languages such as French , is partly arbitrary .", "h": ["nouns"], "t": ["reflexive pronouns"]}]